AI Can Code… But Can It Care? Exploring Automation in Qualitative Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mounting demands for efficiency and productivity in research have created pressures for applied anthropologists to integrate artificial intelligence (AI) into their methodological toolkits. This reflexive essay considers the ethical implications of using AI technologies to code qualitative health data. Drawing on my experience as a medical anthropologist working in a psychiatric epidemiology consortium that shifted from human-driven to AI-driven coding, I suggest that AI cannot attune to the affective, moral, and situated dimensions that bring care to anthropological inquiry. Drawing on a feminist ethic of care, I examine how automation reconfigures economies, ecologies, epistemologies, and relationalities of care in the process of coding. Yet, despite its limitations and harms, an outright rejection of AI forecloses opportunities to imagine new and transformative relationships with this technology. I conclude that care is more than an ethical stance, it is a methodological praxis that requires renewed nurturing as anthropologists working in diverse field sites contend with trends in automation. While anthropologists have studied AI, algorithms, and automation as topical matter, there has not yet been sufficient attention to how AI itself becomes integrated into our own research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it